mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-29 08:27:43 +00:00
Linlang
c8f1c5364a
* change_log_object * lint code * delete comments * change_log_object * change_log_object * fix import test error * update code * update code * fix bugs * skip mypy error * skip mypy error * skip mypy error * Start the flask server before running the demo. * achieve front and back interaction * fix github-advanced-security comments * fix github-advanced-security comments * tmp ignore * fix CI * move some logic * change format * adjust logic * log2json changes * tmp * fix * fix bug * refine log2json between 5 scenarios * fix * refine codes * fix logic * use localhost * add loop & all_duration param for old scenario startup * merge control logic * add README for server ui api * update README * reuse code in logger * add loop_n and all_duration param * fix upload * ui server now use port in setting * fix port setting * fix port setting * fix mypy check * refine logger and log storage * fix ruff error * fix CI * refine logger, loop, storage * bind one FileStorage with one logger * not truncate log storage * refine LoopBase.load(), use `checkout` instead of `output_path` and `do_truncate` * clear session folder when loading loop to run * move component info init step to ExpGen Class * Update rdagent/utils/workflow.py * move truncate_session function to LoopBase class * add checkout param for other scenarios * fix bug * move WebStorage to UI * change web_storage name * add randomname to requirements * add typer * fix requirements --------- Co-authored-by: WinstonLiyte <1957922024@qq.com> Co-authored-by: Bowen Xian <xianbowen@outlook.com> Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>
325 lines
12 KiB
Python
325 lines
12 KiB
Python
from datetime import datetime
|
|
from pathlib import Path
|
|
from typing import Any, Generator
|
|
|
|
import requests
|
|
|
|
from rdagent.log.base import Message, Storage
|
|
from rdagent.log.utils import extract_evoid, extract_loopid_func_name, gen_datetime
|
|
|
|
|
|
class WebStorage(Storage):
|
|
"""
|
|
The storage for web app.
|
|
It is used to provide the data for the web app.
|
|
"""
|
|
|
|
def __init__(self, port: int, path: str) -> None:
|
|
"""
|
|
Initializes the storage object with the specified port and identifier.
|
|
Args:
|
|
port (int): The port number to use for the storage service.
|
|
path (str): The unique identifier for local storage, the log path.
|
|
"""
|
|
self.url = f"http://localhost:{port}"
|
|
self.path = path
|
|
self.msgs = []
|
|
|
|
def __str__(self):
|
|
return f"WebStorage({self.url})"
|
|
|
|
def log(self, obj: object, tag: str, timestamp: datetime | None = None, **kwargs: Any) -> str | Path:
|
|
timestamp = gen_datetime(timestamp)
|
|
|
|
try:
|
|
data = self._obj_to_json(obj=obj, tag=tag, id=self.path, timestamp=timestamp.isoformat())
|
|
if isinstance(data, list):
|
|
for d in data:
|
|
self.msgs.append(d)
|
|
else:
|
|
self.msgs.append(data)
|
|
headers = {"Content-Type": "application/json"}
|
|
requests.post(f"{self.url}/receive", json=data, headers=headers, timeout=1)
|
|
except (requests.ConnectionError, requests.Timeout) as e:
|
|
pass
|
|
|
|
def truncate(self, time: datetime) -> None:
|
|
self.msgs = [m for m in self.msgs if datetime.fromisoformat(m["msg"]["timestamp"]) <= time]
|
|
|
|
def iter_msg(self, **kwargs: Any) -> Generator[Message, None, None]:
|
|
for msg in self.msgs:
|
|
yield Message(
|
|
tag=msg["msg"]["tag"],
|
|
level="INFO",
|
|
timestamp=datetime.fromisoformat(msg["msg"]["timestamp"]),
|
|
content=msg,
|
|
)
|
|
|
|
def _obj_to_json(
|
|
obj: object,
|
|
tag: str,
|
|
id: str,
|
|
timestamp: str,
|
|
) -> list[dict] | dict:
|
|
li, fn = extract_loopid_func_name(tag)
|
|
ei = extract_evoid(tag)
|
|
data = {}
|
|
if "hypothesis generation" in tag:
|
|
from rdagent.core.proposal import Hypothesis
|
|
|
|
h: Hypothesis = obj
|
|
data = {
|
|
"id": id,
|
|
"msg": {
|
|
"tag": "research.hypothesis",
|
|
"timestamp": timestamp,
|
|
"loop_id": li,
|
|
"content": {
|
|
"hypothesis": h.hypothesis,
|
|
"reason": h.reason,
|
|
"concise_reason": h.concise_reason,
|
|
"concise_justification": h.concise_justification,
|
|
"concise_observation": h.concise_observation,
|
|
"concise_knowledge": h.concise_knowledge,
|
|
},
|
|
},
|
|
}
|
|
|
|
elif "experiment generation" in tag:
|
|
from rdagent.components.coder.factor_coder.factor import FactorTask
|
|
from rdagent.components.coder.model_coder.model import ModelTask
|
|
from rdagent.core.experiment import Experiment
|
|
|
|
tasks: list[FactorTask | ModelTask] = obj
|
|
if isinstance(tasks[0], FactorTask):
|
|
data = {
|
|
"id": id,
|
|
"msg": {
|
|
"tag": "research.tasks",
|
|
"timestamp": timestamp,
|
|
"loop_id": li,
|
|
"content": [
|
|
{
|
|
"name": t.factor_name,
|
|
"description": t.factor_description,
|
|
"formulation": t.factor_formulation,
|
|
"variables": t.variables,
|
|
}
|
|
for t in tasks
|
|
],
|
|
},
|
|
}
|
|
elif isinstance(tasks[0], ModelTask):
|
|
data = {
|
|
"id": id,
|
|
"msg": {
|
|
"tag": "research.tasks",
|
|
"timestamp": timestamp,
|
|
"loop_id": li,
|
|
"content": [
|
|
{
|
|
"name": t.name,
|
|
"description": t.description,
|
|
"model_type": t.model_type,
|
|
"formulation": t.formulation,
|
|
"variables": t.variables,
|
|
}
|
|
for t in tasks
|
|
],
|
|
},
|
|
}
|
|
elif "direct_exp_gen" in tag:
|
|
from rdagent.scenarios.data_science.experiment.experiment import (
|
|
DSExperiment,
|
|
)
|
|
|
|
if isinstance(obj, DSExperiment):
|
|
from rdagent.scenarios.data_science.proposal.exp_gen.base import (
|
|
DSHypothesis,
|
|
)
|
|
|
|
h: DSHypothesis = obj.hypothesis
|
|
tasks = [t[0] for t in obj.pending_tasks_list]
|
|
t = tasks[0]
|
|
data = [
|
|
{
|
|
"id": id,
|
|
"msg": {
|
|
"tag": "research.hypothesis",
|
|
"old_tag": tag,
|
|
"timestamp": timestamp,
|
|
"loop_id": li,
|
|
"content": {
|
|
"name_map": {
|
|
"hypothesis": "RD-Agent proposes the hypothesis⬇️",
|
|
"concise_justification": "because the reason⬇️",
|
|
"concise_observation": "based on the observation⬇️",
|
|
"concise_knowledge": "Knowledge⬇️ gained after practice",
|
|
"no_hypothesis": f"No hypothesis available. Trying to construct the first runnable {h.component} component.",
|
|
},
|
|
"hypothesis": h.hypothesis,
|
|
"reason": h.reason,
|
|
"component": h.component,
|
|
"concise_reason": h.concise_reason,
|
|
"concise_justification": h.concise_justification,
|
|
"concise_observation": h.concise_observation,
|
|
"concise_knowledge": h.concise_knowledge,
|
|
},
|
|
},
|
|
},
|
|
{
|
|
"id": id,
|
|
"msg": {
|
|
"tag": "research.tasks",
|
|
"old_tag": tag,
|
|
"timestamp": timestamp,
|
|
"loop_id": li,
|
|
"content": [
|
|
(
|
|
{
|
|
"name": t.name,
|
|
"description": t.description,
|
|
}
|
|
if not hasattr(t, "architecture")
|
|
else {
|
|
"name": t.name,
|
|
"description": t.description,
|
|
"model_type": t.model_type,
|
|
"architecture": t.architecture,
|
|
"hyperparameters": t.hyperparameters,
|
|
}
|
|
)
|
|
],
|
|
},
|
|
},
|
|
]
|
|
elif f"evo_loop_{ei}.evolving code" in tag and "coding" in tag:
|
|
from rdagent.components.coder.factor_coder.factor import FactorFBWorkspace
|
|
from rdagent.components.coder.model_coder.model import (
|
|
ModelFBWorkspace,
|
|
ModelTask,
|
|
)
|
|
from rdagent.core.experiment import FBWorkspace
|
|
|
|
ws: list[FactorFBWorkspace | ModelFBWorkspace] = [i for i in obj]
|
|
if all(isinstance(item, FactorFBWorkspace) for item in ws) or all(
|
|
isinstance(item, ModelFBWorkspace) for item in ws
|
|
):
|
|
data = {
|
|
"id": id,
|
|
"msg": {
|
|
"tag": "evolving.codes",
|
|
"timestamp": timestamp,
|
|
"loop_id": li,
|
|
"evo_id": ei,
|
|
"content": [
|
|
{
|
|
"target_task_name": w.target_task.name,
|
|
"codes": w.file_dict,
|
|
}
|
|
for w in ws
|
|
if w
|
|
],
|
|
},
|
|
}
|
|
elif f"evo_loop_{ei}.evolving feedback" in tag and "coding" in tag:
|
|
from rdagent.components.coder.CoSTEER.evaluators import (
|
|
CoSTEERSingleFeedback,
|
|
)
|
|
|
|
fl: list[CoSTEERSingleFeedback] = [i for i in obj]
|
|
data = {
|
|
"id": id,
|
|
"msg": {
|
|
"tag": "evolving.feedbacks",
|
|
"timestamp": timestamp,
|
|
"loop_id": li,
|
|
"evo_id": ei,
|
|
"content": [
|
|
{
|
|
"final_decision": f.final_decision,
|
|
# "final_feedback": f.final_feedback,
|
|
"execution": f.execution,
|
|
"code": f.code,
|
|
"return_checking": f.return_checking,
|
|
}
|
|
for f in fl
|
|
if f
|
|
],
|
|
},
|
|
}
|
|
elif "scenario" in tag:
|
|
data = {
|
|
"id": id,
|
|
"msg": {
|
|
"tag": "feedback.config",
|
|
"timestamp": timestamp,
|
|
"loop_id": li,
|
|
"content": {"config": obj.experiment_setting},
|
|
},
|
|
}
|
|
|
|
elif "Quantitative Backtesting Chart" in tag:
|
|
import plotly
|
|
|
|
from rdagent.log.ui.qlib_report_figure import report_figure
|
|
|
|
data = {
|
|
"id": id,
|
|
"msg": {
|
|
"tag": "feedback.return_chart",
|
|
"timestamp": timestamp,
|
|
"loop_id": li,
|
|
"content": {"chart_html": plotly.io.to_html(report_figure(obj))},
|
|
},
|
|
}
|
|
elif "running" in tag:
|
|
from rdagent.core.experiment import Experiment
|
|
|
|
if isinstance(obj, Experiment):
|
|
if obj.result is not None:
|
|
result_str = obj.result.to_json()
|
|
data = {
|
|
"id": id,
|
|
"msg": {
|
|
"tag": "feedback.metric",
|
|
"old_tag": tag,
|
|
"timestamp": timestamp,
|
|
"loop_id": li,
|
|
"content": {
|
|
"result": result_str,
|
|
},
|
|
},
|
|
}
|
|
elif "feedback" in tag:
|
|
from rdagent.core.proposal import ExperimentFeedback, HypothesisFeedback
|
|
|
|
ef: ExperimentFeedback = obj
|
|
content = (
|
|
{
|
|
"observations": ef.observations,
|
|
"hypothesis_evaluation": ef.hypothesis_evaluation,
|
|
"new_hypothesis": ef.new_hypothesis,
|
|
"decision": ef.decision,
|
|
"reason": ef.reason,
|
|
"exception": ef.exception,
|
|
}
|
|
if isinstance(ef, HypothesisFeedback)
|
|
else {
|
|
"decision": ef.decision,
|
|
"reason": ef.reason,
|
|
"exception": ef.exception,
|
|
}
|
|
)
|
|
data = {
|
|
"id": id,
|
|
"msg": {
|
|
"tag": "feedback.hypothesis_feedback",
|
|
"timestamp": timestamp,
|
|
"loop_id": li,
|
|
"content": content,
|
|
},
|
|
}
|
|
|
|
return data
|